Abstract
This paper is concerned with a computationally efficient model predictive control (MPC) algorithm for control and set-point computation. The underlying idea consists in integrating the economic optimisation task and manipulated variables calculation by the MPC algorithm into one optimisation problem. The comprehensive nonlinear steady-state model of the process used in economic optimisation is approximated on-line taking into account current state of the plant and disturbance estimate or measurement. Linear and linear quadratic approximations are discussed. The resulting economic optimisation (with the approximate model) is then integrated with standard MPC optimisation problem in such a way that a computationally efficient quadratic programming problem is obtained.
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